基于局部区域拟合模型的磁共振图像分割与偏移估计算法

摘要

Intensity inhomogeneity often exists in Magnetic Resonance ( MR) images, which is due to the smooth bias field caused by the deficiency of the device. Traditional intensity-based segmentation algorithms often assume the uniform intensity belonging to the object and background, respectively. Therefore, these algorithms fail to successfully segment image with intensity inhomogeneity. This paper proposed a local region fitting model for simultaneous segmentation and bias correction. The model is built based on the intensity property in the local region to build an energy function with respect to the intensity, bias field function and the region indicating function. Then, this energy function was optimized with respect to the intensity, bias field and the indicating function, respectively. The segmentation and bias field estimation would be conducted simultaneously finally. The experimental results on the real MR brain images demonstrate the advantages of the proposed method over variational level set approach.%磁共振(MR)图像的灰度通常是不均匀的,这种不均匀性是由于成像设备的缺陷导致产生了一种光滑的偏移场.一般的基于灰度统计特性的分割算法都是假设目标区域和背景区域图像的灰度分别是一致的,因此该类算法不能很好地应用于磁共振图像的分割.提出一种基于局部拟合模型的磁共振图像分割与偏移估计算法:利用图像的局部区域的灰度特性建立恢复图像的灰度、偏移量,以及区域指示函数之间的能量函数,然后分别针对恢复图像的灰度、偏移量以及指示函数进行优化.该算法可以同时对磁共振图像进行分割与偏移估计.实验结果表明该算法优于目前比较流行的磁共振图像分割与去偏移算法如变分水平集方法.